From Movement to Improvement: Locomotive Data in "Retail In-Store" Strategy
Debidutta Barik
Engineering Leader | Generative AI & ML | Data & Platform Engineering | Digital Transformation | Cyber Security | Certified Lean Portfolio Manager| SaFe Agilist | CSPO | CSM
Overview
Retail In-Store includes the strategies, technologies and processes used within physical retail locations to enhance the customer experience, optimize operations, and increase sales.
As the retail industry evolves in the face of digital transformation, the concept of Retail In-Store is becoming increasingly sophisticated, leveraging advanced technologies like Artificial Intelligence (AI) and Machine Learning (ML) to gain competitive advantages in the Retail industry.
How "Retail In-Store" modernizing the Retail Industry through AI/ML & IOT
Technologies & Sample workflow :
Sample code snippet:
1.Analyzing customer movement patterns based on locomotive data to identify high-traffic areas in the store
Data : customer movement like entry/exit and the store sections
Dwell time : Amount of time customer spend in the store.
Section Visits : The number of visits by customer into a specific section
import pandas as pd
import matplotlib.pyplot as plt
# Sample data: Customer movements within the store
data = {
'CustomerID': [1, 2, 3, 4, 5],
'EntryTime': ['10:00', '10:05', '10:10', '10:20', '10:30'],
'ExitTime': ['10:20', '10:25', '10:30', '10:40', '10:50'],
'SectionsVisited': [['A', 'B', 'C'], ['B', 'C'], ['A', 'C'], ['B', 'D'], ['A', 'B', 'D']]
}
# Load data into a DataFrame
df = pd.DataFrame(data)
# Calculate dwell time in minutes
df['DwellTime'] = pd.to_datetime(df['ExitTime'], format='%H:%M') - pd.to_datetime(df['EntryTime'], format='%H:%M')
df['DwellTime'] = df['DwellTime'].dt.total_seconds() / 60
# Count visits per section
section_visits = pd.Series(sum(df['SectionsVisited'], []))
section_visit_counts = section_visits.value_counts()
# Plotting the most visited sections
plt.figure(figsize=(10, 5))
section_visit_counts.plot(kind='bar', color='skyblue')
plt.title('Most Visited Sections in the Store')
plt.xlabel('Store Sections')
plt.ylabel('Number of Visits')
plt.show()
2. A Simulation of an IOT enabled inventory management system tracks stock levels in real time and trigger automatic replenishment.
Inventory : Inventory level of the products
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Replenishment : if the stock levels of any product at any time falls below a specific threshold, the system triggers for refill.
Real-time Update : Stimulate the real time inventory update reflecting sales and replenishment.
import random
# Sample product inventory
inventory = {
'ProductA': 50,
'ProductB': 30,
'ProductC': 20
}
# Replenishment threshold
threshold = 10
# Function to simulate real-time inventory updates
def update_inventory():
for product, stock in inventory.items():
# Randomly decrease stock to simulate sales
inventory[product] -= random.randint(0, 5)
# Check if replenishment is needed
if inventory[product] < threshold:
trigger_replenishment(product)
# Function to trigger replenishment
def trigger_replenishment(product):
print(f"Replenishment triggered for {product}. Current stock: {inventory[product]}")
inventory[product] += 50 # Simulate replenishment
# Simulate inventory updates over time
for _ in range(10): # Simulate 10 time intervals
update_inventory()
print(f"Current inventory: {inventory}")
3. Collaborative filtering to provide personalized product recommendations based on customer purchase behavior
Purchase Data : The data presents whether the customer has purchased the product
Similarity Calculation ( ML Algo) : Cosine similarity is used to calculate the similarity between the customer and their purchase behavior
Product Recommendations : Based on similar customer's purchase, the function recommends similar type of products that a specific customer interested in.
import pandas as pd
from sklearn.metrics.pairwise import cosine_similarity
# Sample purchase data: Rows are customers, columns are products (1 = purchased, 0 = not purchased)
data = {
'ProductA': [1, 0, 0, 1, 0],
'ProductB': [0, 1, 0, 1, 1],
'ProductC': [0, 0, 1, 0, 1],
'ProductD': [1, 1, 0, 0, 0]
}
# Load data into a DataFrame
df = pd.DataFrame(data, index=['Customer1', 'Customer2', 'Customer3', 'Customer4', 'Customer5'])
# Compute cosine similarity between customers
similarity_matrix = cosine_similarity(df)
# Convert similarity matrix to a DataFrame
similarity_df = pd.DataFrame(similarity_matrix, index=df.index, columns=df.index)
# Function to get product recommendations for a customer
def get_recommendations(customer, df, similarity_df):
similar_customers = similarity_df[customer].sort_values(ascending=False).index[1:]
recommended_products = set()
for similar_customer in similar_customers:
purchased_products = df.loc[similar_customer][df.loc[similar_customer] == 1].index
recommended_products.update(purchased_products)
# Exclude products the customer has already purchased
already_purchased = set(df.loc[customer][df.loc[customer] == 1].index)
recommended_products.difference_update(already_purchased)
return list(recommended_products)
# Get recommendations for a specific customer
customer_recommendations = get_recommendations('Customer1', df, similarity_df)
print(f"Recommended products for Customer1: {customer_recommendations}")
Benefits of the Retail In-Store
Please share your experience on modernizing the Retail Instore strategies.
Senior Project Manager at Mindtree
2 个月Very informative and detailed explanation, problem and it's solutions